Carbon footprint

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Carbon footprint: Nike reported TotalCO2e emissions(Direct + Indirect) for the twelve months ending June 30, 2020 at 317 Kt (+12/+4% y-o-y)and plans to reduce emissions 65% by 2030 from a 2015 base year.Thisscience-based targetis aligned withParis Agreementto limit global warming to 1.5 °C above pre-industrial levels.According to a study done in 2017, Nike contributed 3,002,529 metric tons of Carbon Dioxide in 2017 combined from different sectors in the company like retail, manufacturing, management, and more. While emissions of Nike's two corporate jets represent less than 0.1% of its total emissions, they have increased by 20% from 2015 to 2023. Partnership with Newlight In 2021, Nike announced they were working with Newlight Technologies to find more eco-friendly materials for their sneakers. They specifically mentioned Newlight's AirCarbon product which is a bioplastic that can be used to make shoes. The bioplastic is used as a replacement to leather, plastic, and other materials that are like that.Newlight was reported saying that the goal is to reduce Nike's carbon footprint.

[["Nike,_Inc.", "carbon_footprint", "3170000000.0"], ["Nike,_Inc.", "carbon_footprint", "3002529.0"], ["Nike,_Inc.", "carbon_footprint", "\"Nike's two corporate jets represent less than 0.1% of its total emissions, and have increased 20% from 2015 to 2023.\""], ["Nike,_Inc.", "carbon_footprint", "\"Nike plans to reduce emissions 65% by 2030 from a 2015 base year.\""], ["Nike,_Inc.", "carbon_footprint", "\"Nike reported TotalCO2e emissions (Direct + Indirect) for the twelve months ending June 30, 2020 at 317 Kt (+12/+4% y-o-y).\""], ["Nike,_Inc.", "carbon_footprint", "\"Nike is working with Newlight Technologies to find more eco-friendly materials for their sneakers. Newlight's AirCarbon is a bioplastic that can be used to make shoes. The goal is to reduce Nike's carbon footprint.\""], ["Nike,_Inc.", "carbon_footprint", "\"Nike's AirCarbon is a bioplastic that can be used to make shoes. It is a replacement for leather, plastic, and other materials like that.\""], ["Nike,_Inc.", "carbon_footprint", "\"Nike's science-based target is aligned with the Paris Agreement to limit global warming to 1.5 °C above pre-industrial levels.\""]]


Sustainability

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Sustainability: Nike has taken steps to reduce its environmental impact. It has worked to reduce carbon emissions nearly 3% across its value chain from its FY11 baseline,and sourced from fewer, higher-performing contract factories. In 2019, Nike began a program called "Move to Zero" in an effort to achieve zero waste and zero carbon in the organization's supply chain and product lifetime.The men's and women's sections of the collection contain at least 60% organic and recycled materials, including sustainably sourced cotton.

[["Nike,_Inc", "Company"], ["Move_to_Zero", "Sustainability"], ["2019", "Year"], ["Nike,_Inc", "Product"], ["60.0", "number"], ["Organic_cotton", "Sustainability"], ["Nike,_Inc", "Product"], ["Men's_college_basketball", "Product"], ["Women's_college_basketball", "Product"], ["Nike,_Inc", "Company"], ["\"Reduce carbon emissions across our value chain by 3.0 percent from our FY11 baseline\"", "Sustainability"]]


Sustainability

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Sustainability: Nike has taken steps to reduce its environmental impact. It has worked to reduce carbon emissions nearly 3% across its value chain from its FY11 baseline,and sourced from fewer, higher-performing contract factories. In 2019, Nike began a program called "Move to Zero" in an effort to achieve zero waste and zero carbon in the organization's supply chain and product lifetime.The men's and women's sections of the collection contain at least 60% organic and recycled materials, including sustainably sourced cotton.

[["Nike,_Inc.", "has_product_line", "Nike_Men's_Collection"], ["Nike,_Inc.", "has_product_line", "Nike_Women's_Collection"], ["Nike,_Inc.", "sustainability", "\"Nike has taken steps to reduce its environmental impact. It has worked to reduce carbon emissions nearly 3% across its value chain from its FY11 baseline, and sourced from fewer, higher-performing contract factories.\""], ["Nike,_Inc.", "sustainability", "\"In 2019, Nike began a program called 'Move to Zero' in an effort to achieve zero waste and zero carbon in the organization's supply chain and product lifetime.\""], ["Nike,_Inc.", "sustainability", "\"The men's and women's sections of the collection contain at least 60% organic and recycled materials, including sustainably sourced cotton.\""]]


Marketing strategy

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Marketing strategy: Nike promotes its products throughsponsorship agreementswith celebrity athletes, professional teams and college athletic teams. Nike has endorsement deals with many top sports players such asLeBron James,Kevin Durant, andSerena Williams.

[["Nike,_Inc", "Company"], ["Marketing", "Product"], ["Sponsorship", "Product"], ["LeBron_James", "Person"], ["Serena_Williams", "Person"], ["Kevin_Durant", "Person"]]


Marketing strategy

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Marketing strategy: Nike promotes its products throughsponsorship agreementswith celebrity athletes, professional teams and college athletic teams. Nike has endorsement deals with many top sports players such asLeBron James,Kevin Durant, andSerena Williams.

[["Nike,_Inc", "marketing_strategy", "\"Nike promotes its products through sponsorship agreements with celebrity athletes, professional teams and college athletic teams.\""], ["Nike,_Inc", "has_endorsement_deal", "LeBron_James"], ["Nike,_Inc", "has_endorsement_deal", "Kevin_Durant"], ["Nike,_Inc", "has_endorsement_deal", "Serena_Williams"]]


Advertising

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Advertising: In 1982, Nike aired its first three national television ads, created by newly formed ad agencyWieden+Kennedy(W+K), during the broadcast of theNew York Marathon.The Cannes Advertising Festival has named Nike its Advertiser of the Year in 1994 and 2003, making it the first company to receive that honor twice. Nike also has earned theEmmy Awardfor best commercial in 2000 and 2002. The first was for "The Morning After," a satirical look at what a runner might face on the morning of January 1, 2000, if every dire prediction about theY2K problemcame to fruition.The second was for a 2002 spot called "Move," which featured a series of famous and everyday athletes in a variety of athletic pursuits. Nike was criticized for its use of theBeatlessong "Revolution" in a 1987 commercial against the wishes ofApple Records, the Beatles' recording company. Nike paid US$250,000 toCapitol RecordsInc., which held the North American licensing rights to the recordings, for the right to use the Beatles' rendition for a year. That same year, Apple Records sued Nike Inc., Capitol Records Inc.,EMI RecordsInc. and Wieden+Kennedy for $15 million.Capitol-EMI countered by saying the lawsuit was "groundless" because Capitol had licensed the use of "Revolution" with the "active support and encouragement ofYoko Ono, a shareholder and director of Apple Records." Nike discontinued airing ads featuring "Revolution" in March 1988. Yoko Ono later gave permission to Nike to useJohn Lennon's "Instant Karma" in another advertisement. Nike was an early adopter ofinternet marketing,email managementtechnologies, and usingbroadcastandnarrowcastcommunication technologies to create multimedia marketing campaigns. In late June 2005, Nike received criticism fromIan MacKaye, owner ofDischord Records, guitarist/vocalist forFugaziandThe Evens, and front man of the defunct punk bandMinor Threat, for appropriating imagery and text from Minor Threat's1981 self-titled album's cover art in a flyer promotingNike Skateboarding's 2005 East Coast demo tour. On June 27, Nike Skateboarding's website issued an apology to Dischord, Minor Threat, and fans of both and announced that they have tried to remove and dispose of all flyers. They stated that the people who designed it were skateboarders and Minor Threat fans themselves who created the advertisement out of respect and appreciation for the band.The dispute was eventually settled out of court between Nike and Minor Threat. As part of the 6.0 campaign, Nike introduced a new line of T-shirts that include phrases such as "Dope", "Get High" and "Ride Pipe" – sports lingo that is also adouble entendrefordrug use. Boston MayorThomas Meninoexpressed his objection to the shirts after seeing them in a window display at the city's Niketown and asked the store to remove the display. "What we don't need is a major corporation like Nike, which tries to appeal to the younger generation, out there giving credence to the drug issue," Menino toldThe Boston Herald. A company official stated the shirts were meant to pay homage to extreme sports, and that Nike does not condone the illegal use of drugs.Nike was forced to replace the shirt line.

[["Nike,_Inc.", "Company"], ["Wieden+Kennedy", "Advertising"], ["The_New_York_Marathon", "Event"], ["The_Cannes_Advertising_Festival", "Award"], ["2000", "Year"], ["Emmy_Award", "Award"], ["2002", "Year"], ["The_Morning_After", "Film"], ["Y2K_problem", "Problem"], ["Apple_Records", "Company"], ["Nike,_Inc._v._Apple_Records", "Legal_case"], ["Revolution_(The_Beatles_song)", "MusicalWork"], ["Capitol_Records", "Company"], ["United_States", "Country"], ["Minor_Threat", "MusicalWork"], ["Nike,_Inc._v._Apple_Records", "Company"], ["Nike,_Inc.", "MusicalWork"], ["Ian_MacKaye", "Person"], ["Nike,_Inc.", "MusicalWork"], ["Nike_Skateboarding", "Company"], ["2005", "Year"], ["Nike,_Inc.", "MusicalWork"], ["Boston", "Place"], ["Thomas_Menino", "Person"], ["The_Boston_Herald", "Company"], ["6.0_(Nike)", "Product"], ["Dope_(drug)", "MusicalWork"], ["Get_high", "MusicalWork"], ["Ride_the_pipe", "MusicalWork"]]


Advertising

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Advertising: In 1982, Nike aired its first three national television ads, created by newly formed ad agencyWieden+Kennedy(W+K), during the broadcast of theNew York Marathon.The Cannes Advertising Festival has named Nike its Advertiser of the Year in 1994 and 2003, making it the first company to receive that honor twice. Nike also has earned theEmmy Awardfor best commercial in 2000 and 2002. The first was for "The Morning After," a satirical look at what a runner might face on the morning of January 1, 2000, if every dire prediction about theY2K problemcame to fruition.The second was for a 2002 spot called "Move," which featured a series of famous and everyday athletes in a variety of athletic pursuits. Nike was criticized for its use of theBeatlessong "Revolution" in a 1987 commercial against the wishes ofApple Records, the Beatles' recording company. Nike paid US$250,000 toCapitol RecordsInc., which held the North American licensing rights to the recordings, for the right to use the Beatles' rendition for a year. That same year, Apple Records sued Nike Inc., Capitol Records Inc.,EMI RecordsInc. and Wieden+Kennedy for $15 million.Capitol-EMI countered by saying the lawsuit was "groundless" because Capitol had licensed the use of "Revolution" with the "active support and encouragement ofYoko Ono, a shareholder and director of Apple Records." Nike discontinued airing ads featuring "Revolution" in March 1988. Yoko Ono later gave permission to Nike to useJohn Lennon's "Instant Karma" in another advertisement. Nike was an early adopter ofinternet marketing,email managementtechnologies, and usingbroadcastandnarrowcastcommunication technologies to create multimedia marketing campaigns. In late June 2005, Nike received criticism fromIan MacKaye, owner ofDischord Records, guitarist/vocalist forFugaziandThe Evens, and front man of the defunct punk bandMinor Threat, for appropriating imagery and text from Minor Threat's1981 self-titled album's cover art in a flyer promotingNike Skateboarding's 2005 East Coast demo tour. On June 27, Nike Skateboarding's website issued an apology to Dischord, Minor Threat, and fans of both and announced that they have tried to remove and dispose of all flyers. They stated that the people who designed it were skateboarders and Minor Threat fans themselves who created the advertisement out of respect and appreciation for the band.The dispute was eventually settled out of court between Nike and Minor Threat. As part of the 6.0 campaign, Nike introduced a new line of T-shirts that include phrases such as "Dope", "Get High" and "Ride Pipe" – sports lingo that is also adouble entendrefordrug use. Boston MayorThomas Meninoexpressed his objection to the shirts after seeing them in a window display at the city's Niketown and asked the store to remove the display. "What we don't need is a major corporation like Nike, which tries to appeal to the younger generation, out there giving credence to the drug issue," Menino toldThe Boston Herald. A company official stated the shirts were meant to pay homage to extreme sports, and that Nike does not condone the illegal use of drugs.Nike was forced to replace the shirt line.

[["Nike,_Inc.", "has_product_line", "Nike_Skateboarding"], ["Nike,_Inc.", "launched_campaign", "6.0_campaign"], ["Nike,_Inc.", "has_product_line", "T-shirt"], ["Nike,_Inc.", "advertising", "\"Dope\", \"Get High\", \"Ride Pipe\""], ["Nike,_Inc.", "advertising", "\"The Morning After\""], ["Nike,_Inc.", "advertising", "\"Move\""], ["Nike,_Inc.", "advertising", "Ian_MacKaye"], ["Nike,_Inc.", "advertising", "Yoko_Ono"], ["Nike,_Inc.", "advertising", "The_Beatles"], ["Nike,_Inc.", "advertising", "Revolution_(The_Beatles_song)"], ["Nike,_Inc.", "advertising", "Emmy_Award"], ["Nike,_Inc.", "advertising", "Boston"], ["Nike,_Inc.", "advertising", "Thomas_Menino"], ["Nike,_Inc.", "advertising", "Nike,_Inc._v._Apple_Records,_Inc._et_al."], ["Nike,_Inc.", "advertising", "Capitol_Records,_Inc."], ["Nike,_Inc.", "advertising", "Fugazi"], ["Nike,_Inc.", "advertising", "The_Evens"], ["Nike,_Inc.", "advertising", "Minor_Threat"], ["Nike,_Inc.", "advertising", "Nike_Skateboarding_2005_East_Coast_demo_tour"], ["Nike,_Inc.", "advertising", "Dischord_Records"], ["Nike,_Inc.", "advertising", "John_Lennon"]]


NBA uniform deal

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, NBA uniform deal: In June 2015, Nike signed an 8-year deal with theNBAto become the official uniform supplier for the league, beginning with the 2017–18 season.The brand took over forAdidas, who provided the uniforms for the league since 2006.Unlike previous deals, Nike's logo appear on NBA jerseys – a first for the league.Initially, theCharlotte Hornets, owned by longtime Nike endorserMichael Jordan, were the only team not to sport the Nike swoosh, instead wearing theJumpmanlogo associated with Jordan-related merchandise.However, beginning with the 2020–21 season, the Jumpman replaced the swoosh on the NBA's alternate "Statement" uniforms.

[["Nike,_Inc.", "company"], ["NBA", "product"], ["2017–18_NBA_season", "date"], ["Adidas", "company"], ["Michael_Jordan", "person"], ["Charlotte_Hornets", "team"], ["Jumpman", "brand"], ["2020–21_NBA_season", "date"]]


NBA uniform deal

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, NBA uniform deal: In June 2015, Nike signed an 8-year deal with theNBAto become the official uniform supplier for the league, beginning with the 2017–18 season.The brand took over forAdidas, who provided the uniforms for the league since 2006.Unlike previous deals, Nike's logo appear on NBA jerseys – a first for the league.Initially, theCharlotte Hornets, owned by longtime Nike endorserMichael Jordan, were the only team not to sport the Nike swoosh, instead wearing theJumpmanlogo associated with Jordan-related merchandise.However, beginning with the 2020–21 season, the Jumpman replaced the swoosh on the NBA's alternate "Statement" uniforms.

[["NBA", "uniformSupplier", "Nike,_Inc."], ["Nike,_Inc.", "hasProduct", "NBA"], ["NBA", "season", "2017–18_NBA_season"], ["NBA", "logo", "Jumpman"], ["NBA", "season", "2020–21_NBA_season"], ["NBA", "uniformSupplier", "Adidas"], ["NBA", "launchDate", "2015-06-23"], ["NBA", "hasProduct", "NBA_uniform"], ["Charlotte_Hornets", "hasProduct", "Jumpman"], ["Michael_Jordan", "hasProduct", "Nike,_Inc."]]


Sponsorship

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Sponsorship: Nike sponsors top athletes in many sports to use their products and promote and advertise their technology and design. Nike's first professional athlete endorser was Romanian tennis playerIlie Năstase.The first track endorser was distance runnerSteve Prefontaine. Prefontaine was the prized pupil of the company's co-founder,Bill Bowerman, while he coached at the University of Oregon. Today, the Steve Prefontaine Building is named in his honor at Nike's corporate headquarters. Nike has only made one statue of its sponsored athletes and it is of Steve Prefontaine. Nike has also sponsored many other successfultrack and fieldathletes over the years, such asSebastian Coe,Carl Lewis,Jackie Joyner-Kersee,Michael JohnsonandAllyson Felix. The signing of basketball playerMichael Jordanin 1984, with his subsequent promotion of Nike over the course of his career, withSpike LeeasMars Blackmon, proved to be one of the biggest boosts to Nike's publicity and sales. Nike is a major sponsor of the athletic programs atPenn State Universityand named its first child care facility afterJoe Paternowhen it opened in 1990 at the company's headquarters. Nike originally announced it would not remove Paterno's name from the building in the wake of the Penn State sex abuse scandal. After the Freeh Report was released on July 12, 2012, Nike CEO Mark Parker announced the name Joe Paterno would be removed immediately from the child development center. A new name has yet to be announced. In the early 1990s, Nike made a strong push into the soccer business making endorsement deals with famous and charismatic players such asRomário,Eric CantonaorEdgar Davids. They continued the growth in the sport by signing more top players including:Ronaldo,Ronaldinho,Francesco Totti,Thierry Henry,Didier Drogba,Andrés Iniesta,Wayne Rooneyand still have many of the sport's biggest stars under their name, withCristiano Ronaldo,Zlatan Ibrahimović,Neymar,Harry Kane,Eden HazardandKylian Mbappéamong others.A Barcelona prodigy,Lionel Messihad been signed with Nike since age 14, but transferred to Adidas after they successfully challenged their rival's claim to his image rights in court. Nike has been the official ball supplier for thePremier Leaguesince the 2000–01 season.In 2012, Nike carried a commercial partnership with theAsian Football Confederation.In August 2014, Nike announced that they will not renew their kit supply deal withManchester Unitedafter the 2014–15 season, citing rising costs.Since the start of the 2015–16 season,Adidashas manufactured Manchester United's kit as part of a world-record 10-year deal worth a minimum of £750 million. Nike still has many of the top teams playing in their uniforms, including:FC Barcelona,Paris Saint-GermainandLiverpool(the latter from the 2020–21 season),and the national teams of Brazil, France, England, Uruguay and the Netherlands among many others. Nike has been the sponsor for many top ranked tennis players. Brand's commercial success in the sport went hand in hand with the endorsement deals signed with the biggest and the world's most charismatic stars and number one ranked players of the subsequent eras, includingJohn McEnroein the 1980s,Andre AgassiandPete Samprasin the 1990s andRoger Federer,Rafael Nadal,Serena WilliamsandMaria Sharapovawith the start of the 21st century. Nike sponsoredTiger Woodsuntil 2024,and remained on his side amid the controversies that shaped the golfer's career.In January 2013, Nike signedRory McIlroy, the then No 1 golfer in the world to a 10-year sponsorship deal worth $250 million.Nike has also gone on to sign top players in golf includingScottie Scheffler,Brooks Koepka,Nelly Korda,Tommy Fleetwood,Tony FinauandCam Davis. Nike’s 2016 decision to exit the golf equipment business–such as club manufacturing–due to an 8.2% decline sales in one year meant sponsored athletes exclusively wore Nike apparel. Nike was the official kit sponsor for theIndian cricket teamfrom 2005 to 2020.On February 21, 2013, Nike announced it suspended its contract with South African limbless athleteOscar Pistorius, due to him being charged withpremeditated murder. Nike consolidated its position in basketball in 2015 when it was announced that the company would sign an 8-year deal with the NBA, taking over from the league's previous uniform sponsor, Adidas. The deal required all franchise team members to wear jerseys and shorts with the Swoosh logo, beginning with the 2017/18 season.After the success of partnership with Jordan, which resulted in the creation of the uniqueAir Jordanbrand, Nike has continued to build partnership with the biggest names in basketball. LeBron James was given the Slogan "We are All Witnesses" when he signed with Nike. Similar to "Air Jordan", James' brand became massively popular.Some have had signature shoes designed for them, includingKobe Bryant,Jason Kidd,Vince Carterand more recently, James andKevin Durant,Giannis Antetokounmpo,Jayson Tatum,Paul GeorgeandLuka Dončić, among others. Nike recently made signature shoes for WNBA stars as well, as the leagues popularity takes off. Although a dozen women have received signature sneakers in the WNBA's 27-year history, it had been over a decade since a woman had received a signature sneaker. Nike's first signature shoe in the WNBA was withSheryl Swoops, and since then they have made signature silhouettes forLisa Leslie,Dawn Staley,Cynthia Cooper, and most recently forSabrina Ionescu.Caitlin Clarkwill also receive a signature shoe deal as part of her eight-year, 28 million dollar deal. A news report originating from CNN reported that Nike spent $11.5 billion, nearly a third of its sales, on marketing and endorsement contracts in the year 2018. Nike and its Jordan brand sponsored 85 men's and women's basketball teams in the NCAA tournament.

[["Nike", "Company"], ["John McEnroe", "Person"], ["Andre_Agassi", "Person"], ["Pete_Sampras", "Person"], ["Tiger_Woods", "Person"], ["Rory_McIlroy", "Person"], ["Oscar_Pistorius", "Person"], ["Scottie_Scheffler", "Person"], ["Brooks_Koepka", "Person"], ["Nelly_Korda", "Person"], ["Tommy_Fleetwood", "Person"], ["Tony_Finau", "Person"], ["Caitlin_Clark", "Person"], ["Giannis_Antetokounmpo", "Person"], ["Luka_Dončić", "Person"], ["Paul_George", "Person"], ["Sheryl_Swoopes", "Person"], ["Sabrina_Ionescu", "Person"], ["LeBron_James", "Person"], ["Vince_Carter", "Person"], ["Cynthia_Cooper", "Person"], ["Dawn_Staley", "Person"], ["Air_Jordan", "Product"], ["Nike", "Product"], ["Jordan_Brand", "Company"], ["McEnroe", "Product"], ["Agassi", "Product"], ["Sampras", "Product"], ["Tiger_Woods", "Product"], ["Rory_McIlroy", "Product"], ["Oscar_Pistorius", "Product"], ["Scottie_Scheffler", "Product"], ["Brooks_Koepka", "Product"], ["Nelly_Korda", "Product"], ["Tommy_Fleetwood", "Product"], ["Tony_Finau", "Product"], ["Caitlin_Clark", "Product"], ["Giannis_Antetokounmpo", "Product"], ["Luka_Dončić", "Product"], ["Paul_George", "Product"], ["Sheryl_Swoopes", "Product"], ["Sabrina_Ionescu", "Product"], ["LeBron_James", "Product"], ["Vince_Carter", "Product"], ["Cynthia_Cooper", "Product"], ["Dawn_Staley", "Product"], ["Nike", "Athlete"], ["Jordan_Brand", "Athlete"], ["McEnroe", "Athlete"], ["Agassi", "Athlete"], ["Sampras", "Athlete"], ["Tiger_Woods", "Athlete"], ["Rory_McIlroy", "Athlete"], ["Oscar_Pistorius", "Athlete"], ["Scottie_Scheffler", "Athlete"], ["Brooks_Koepka", "Athlete"], ["Nelly_Korda", "Athlete"], ["Tommy_Fleetwood", "Athlete"], ["Tony_Finau", "Athlete"], ["Caitlin_Clark", "Athlete"], ["Giannis_Antetokounmpo", "Athlete"], ["Luka_Dončić", "Athlete"], ["Paul_George", "Athlete"], ["Sheryl_Swoopes", "Athlete"], ["Sabrina_Ionescu", "Athlete"], ["LeBron_James", "Athlete"], ["Vince_Carter", "Athlete"], ["Cynthia_Cooper", "Athlete"], ["Dawn_Staley", "Athlete"], 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Sponsorship

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is RE: Relation Extraction.

Here is an example of task execution:

{'Schema': [{'designed': {'domain': 'Person', 'range': 'Structure'}}, {'located_in': {'domain': 'Structure', 'range': 'Location'}}, {'part_of': {'domain': 'City', 'range': 'Country'}}, {'friends_with': {'domain': 'Person', 'range': 'Person'}}, {'lives_at': {'domain': 'Person', 'range': 'Location'}}, {'visits': {'domain': 'Person', 'range': 'Location'}}, {'accompanied_by': {'domain': 'Person', 'range': 'Person'}}, {'shares': {'domain': 'Person', 'range': 'Product'}}], 'Text': 'Gustave Eiffel designed the Eiffel Tower, which is located in Paris, France.', 'Relations': [['Gustave Eiffel', 'designed', 'Eiffel Tower'], ['Eiffel Tower', 'located_in', 'Paris'], ['Paris', 'part_of', 'France']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Subject", "Relation", "Object"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: [{"part_of": {"domain": "Organization", "range": "Organization"}}, {"parent_company_of": {"domain": "Organization", "range": "Organization"}}, {"subsidiary_of": {"domain": "Organization", "range": "Organization"}}, {"acquired": {"domain": "Organization", "range": "Organization"}}, {"divested": {"domain": "Organization", "range": "Organization"}}, {"owns_brand": {"domain": "Organization", "range": "Product/Brand"}}, {"holds_stake_in": {"domain": "Organization", "range": "Organization"}}, {"spun_off": {"domain": "Organization", "range": "Organization"}}, {"formed_from": {"domain": "Organization", "range": "Organization"}}, {"founded_by": {"domain": "Organization", "range": "Person"}}, {"has_CEO": {"domain": "Organization", "range": "Person"}}, {"manufactures": {"domain": "Organization", "range": "ProductType"}}, {"develops": {"domain": "Organization", "range": "ProductType"}}, {"produces": {"domain": "Organization", "range": "ProductType"}}, {"sells": {"domain": "Organization", "range": "ProductType"}}, {"has_product_line": {"domain": "Organization", "range": "Product/Brand"}}, {"is_a_type_of": {"domain": "Product/Brand", "range": "ProductType"}}, {"is_brand_of": {"domain": "Product/Brand", "range": "Organization"}}, {"features_technology": {"domain": "Product/Brand", "range": "Technology"}}, {"uses_material": {"domain": "Product/Brand", "range": "Material"}}, {"launched_product": {"domain": "Organization", "range": "Product/Brand"}}, {"had_revenue_of": {"domain": "Organization", "range": "Amount"}}, {"had_profit_of": {"domain": "Organization", "range": "Amount"}}, {"in_year": {"domain": "FinancialMetric", "range": "Year"}}, {"market_value_of": {"domain": "Organization", "range": "Amount"}}, {"is_publicly_traded": {"domain": "Organization", "range": "Boolean"}}, {"listed_on_exchange": {"domain": "Organization", "range": "StockExchange"}}, {"experienced_growth": {"domain": "Organization", "range": "FinancialMetric"}}, {"occurred_in_year": {"domain": "Event/Action", "range": "Year"}}, {"occurred_on_date": {"domain": "Event/Action", "range": "SpecificDate"}}, {"resulted_in": {"domain": "Event/Action", "range": "Outcome"}}, {"preceded": {"domain": "Event/Action", "range": "Event/Action"}}, {"followed_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"marked_milestone": {"domain": "Event/Action", "range": "Organization"}}, {"influenced_by": {"domain": "Event/Action", "range": "Event/Action"}}, {"led_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"was_a_response_to": {"domain": "Event/Action", "range": "Event/Action"}}, {"has_trademark": {"domain": "Organization", "range": "Trademark"}}, {"uses_logo": {"domain": "Organization", "range": "Logo"}}, {"acquired_trademark_from": {"domain": "Organization", "range": "Organization"}}, {"is_known_for": {"domain": "Organization", "range": "Characteristic"}}, {"launched_campaign": {"domain": "Organization", "range": "BusinessConcept"}}]
Text: article: Nike,_Inc, Sponsorship: Nike sponsors top athletes in many sports to use their products and promote and advertise their technology and design. Nike's first professional athlete endorser was Romanian tennis playerIlie Năstase.The first track endorser was distance runnerSteve Prefontaine. Prefontaine was the prized pupil of the company's co-founder,Bill Bowerman, while he coached at the University of Oregon. Today, the Steve Prefontaine Building is named in his honor at Nike's corporate headquarters. Nike has only made one statue of its sponsored athletes and it is of Steve Prefontaine. Nike has also sponsored many other successfultrack and fieldathletes over the years, such asSebastian Coe,Carl Lewis,Jackie Joyner-Kersee,Michael JohnsonandAllyson Felix. The signing of basketball playerMichael Jordanin 1984, with his subsequent promotion of Nike over the course of his career, withSpike LeeasMars Blackmon, proved to be one of the biggest boosts to Nike's publicity and sales. Nike is a major sponsor of the athletic programs atPenn State Universityand named its first child care facility afterJoe Paternowhen it opened in 1990 at the company's headquarters. Nike originally announced it would not remove Paterno's name from the building in the wake of the Penn State sex abuse scandal. After the Freeh Report was released on July 12, 2012, Nike CEO Mark Parker announced the name Joe Paterno would be removed immediately from the child development center. A new name has yet to be announced. In the early 1990s, Nike made a strong push into the soccer business making endorsement deals with famous and charismatic players such asRomário,Eric CantonaorEdgar Davids. They continued the growth in the sport by signing more top players including:Ronaldo,Ronaldinho,Francesco Totti,Thierry Henry,Didier Drogba,Andrés Iniesta,Wayne Rooneyand still have many of the sport's biggest stars under their name, withCristiano Ronaldo,Zlatan Ibrahimović,Neymar,Harry Kane,Eden HazardandKylian Mbappéamong others.A Barcelona prodigy,Lionel Messihad been signed with Nike since age 14, but transferred to Adidas after they successfully challenged their rival's claim to his image rights in court. Nike has been the official ball supplier for thePremier Leaguesince the 2000–01 season.In 2012, Nike carried a commercial partnership with theAsian Football Confederation.In August 2014, Nike announced that they will not renew their kit supply deal withManchester Unitedafter the 2014–15 season, citing rising costs.Since the start of the 2015–16 season,Adidashas manufactured Manchester United's kit as part of a world-record 10-year deal worth a minimum of £750 million. Nike still has many of the top teams playing in their uniforms, including:FC Barcelona,Paris Saint-GermainandLiverpool(the latter from the 2020–21 season),and the national teams of Brazil, France, England, Uruguay and the Netherlands among many others. Nike has been the sponsor for many top ranked tennis players. Brand's commercial success in the sport went hand in hand with the endorsement deals signed with the biggest and the world's most charismatic stars and number one ranked players of the subsequent eras, includingJohn McEnroein the 1980s,Andre AgassiandPete Samprasin the 1990s andRoger Federer,Rafael Nadal,Serena WilliamsandMaria Sharapovawith the start of the 21st century. Nike sponsoredTiger Woodsuntil 2024,and remained on his side amid the controversies that shaped the golfer's career.In January 2013, Nike signedRory McIlroy, the then No 1 golfer in the world to a 10-year sponsorship deal worth $250 million.Nike has also gone on to sign top players in golf includingScottie Scheffler,Brooks Koepka,Nelly Korda,Tommy Fleetwood,Tony FinauandCam Davis. Nike’s 2016 decision to exit the golf equipment business–such as club manufacturing–due to an 8.2% decline sales in one year meant sponsored athletes exclusively wore Nike apparel. Nike was the official kit sponsor for theIndian cricket teamfrom 2005 to 2020.On February 21, 2013, Nike announced it suspended its contract with South African limbless athleteOscar Pistorius, due to him being charged withpremeditated murder. Nike consolidated its position in basketball in 2015 when it was announced that the company would sign an 8-year deal with the NBA, taking over from the league's previous uniform sponsor, Adidas. The deal required all franchise team members to wear jerseys and shorts with the Swoosh logo, beginning with the 2017/18 season.After the success of partnership with Jordan, which resulted in the creation of the uniqueAir Jordanbrand, Nike has continued to build partnership with the biggest names in basketball. LeBron James was given the Slogan "We are All Witnesses" when he signed with Nike. Similar to "Air Jordan", James' brand became massively popular.Some have had signature shoes designed for them, includingKobe Bryant,Jason Kidd,Vince Carterand more recently, James andKevin Durant,Giannis Antetokounmpo,Jayson Tatum,Paul GeorgeandLuka Dončić, among others. Nike recently made signature shoes for WNBA stars as well, as the leagues popularity takes off. Although a dozen women have received signature sneakers in the WNBA's 27-year history, it had been over a decade since a woman had received a signature sneaker. Nike's first signature shoe in the WNBA was withSheryl Swoops, and since then they have made signature silhouettes forLisa Leslie,Dawn Staley,Cynthia Cooper, and most recently forSabrina Ionescu.Caitlin Clarkwill also receive a signature shoe deal as part of her eight-year, 28 million dollar deal. A news report originating from CNN reported that Nike spent $11.5 billion, nearly a third of its sales, on marketing and endorsement contracts in the year 2018. Nike and its Jordan brand sponsored 85 men's and women's basketball teams in the NCAA tournament.

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Ties with the University of Oregon

company product service industry brand country location organization person founder position event action founding accusition merger partnership expansion restructuring divestment sale bankruptcy financial_metric business_concept revenue profit loss investment funding market_share competition market_trend regulation innovation sustainability corporate_social_responsibility award date/time year period

Prompt:
Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data. The goal is to transform raw text into meaningful data that can be easily analyzed and used in various applications. Named Entity Recognition (NER) and Relation Extraction are two common tasks in Information Extraction.

Named Entity Recognition involves identifying and classifying named entities such as people, organizations, locations, and other domain-specific concepts. Relation Extraction involves identifying and extracting relationships between entities.

The task at hand is NER: Named Entity Recognition.

Here is an example of task execution:

{'Schema': ['Person', 'Organization', 'Location', 'Date', 'Product'], 'Text': 'The Eiffel Tower in Paris, designed by Gustave Eiffel, is one of the most famous landmarks in France.', 'Entities': [['Eiffel Tower', 'Location'], ['Paris', 'Location'], ['Gustave Eiffel', 'Person'], ['France', 'Location']]}

Analyze the text and targets carefully, identify relevant information.

Extract the information in the following format: `[["Entity", "Type"], ...]`. If no matching entities are found, return an **empty list**. Please provide only the extracted information without any explanations.

Schema: ["company", "product", "service", "industry", "brand", "country", "location", "organization", "person", "founder", "position", "event", "action", "founding", "accusition", "merger", "partnership", "expansion", "restructuring", "divestment", "sale", "bankruptcy", "financial_metric", "business_concept", "revenue", "profit", "loss", "investment", "funding", "market_share", "competition", "market_trend", "regulation", "innovation", "sustainability", "corporate_social_responsibility", "award", "date/time", "year", "period"]
Text: article: Nike,_Inc, Ties with the University of Oregon: Nike maintains strong ties, both directly and through partnerships withPhil Knight, with theUniversity of Oregon.Nike designs the University of Oregon football program's team attire.New unique combinations are issued before every game day.Tinker Hatfield, who also redesigned the university's logo, leads this effort. More recently, the corporation donated $13.5 million towards the renovation and expansion ofHayward Field. Phil Knight has invested substantial personal funds towards developing and maintaining the university's athletic apparatus.His university projects often involve input from Nike designers and executives, such asTinker Hatfield.

[["Nike,_Inc.", "Company"], ["Phil_Knight", "Person"], ["University_of_Oregon", "University"], ["Nike,_Inc.", "University"], ["Tinker_Hatfield", "Person"], ["Hayward_Field", "Building"], ["13.5 (million _dollars)", "number"], ["University_of_Oregon_football", "Product"], ["Nike,_Inc.", "University"], ["Phil_Knight", "Person"], ["Tinker_Hatfield", "Person"]]